{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55bbe2cc"
      },
      "source": [
        "# ECG - Arrythmia Classification - (Physionet - MIT-BIH )"
      ],
      "id": "55bbe2cc"
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ZROgFOSQ1iuz"
      },
      "id": "ZROgFOSQ1iuz"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3f7a17dd",
        "outputId": "160df41e-18f4-4d39-a658-2ab86efdbd7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/MyDrive/ECG\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/MyDrive/ECG"
      ],
      "id": "3f7a17dd"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc34104d",
        "outputId": "b513cc24-2767-4886-e860-1d5627577bcc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: wfdb in /usr/local/lib/python3.10/dist-packages (4.1.2)\n",
            "Requirement already satisfied: SoundFile>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from wfdb) (0.12.1)\n",
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.10/dist-packages (from wfdb) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.10.1 in /usr/local/lib/python3.10/dist-packages (from wfdb) (1.23.5)\n",
            "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from wfdb) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from wfdb) (2.31.0)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wfdb) (1.11.4)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (1.2.0)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (9.4.0)\n",
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            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->wfdb) (2023.3.post1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.8.1->wfdb) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.8.1->wfdb) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.8.1->wfdb) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.8.1->wfdb) (2023.11.17)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from SoundFile>=0.10.0->wfdb) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->SoundFile>=0.10.0->wfdb) (2.21)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.2.2->wfdb) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "pip install wfdb"
      ],
      "id": "cc34104d"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "165ff785"
      },
      "outputs": [],
      "source": [
        "import wfdb\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "from scipy.signal import savgol_filter, find_peaks\n",
        "from scipy.interpolate import interp1d\n",
        "from scipy.interpolate import interp1d\n",
        "from scipy.signal import butter, filtfilt\n",
        "\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from collections import Counter\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import StratifiedKFold"
      ],
      "id": "165ff785"
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the Butterworth filter\n",
        "def butter_lowpass(cutoff, fs, order=5):\n",
        "    nyq = 0.5 * fs\n",
        "    normal_cutoff = cutoff / nyq\n",
        "    b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
        "    return b, a\n",
        "\n",
        "def butter_lowpass_filter(data, cutoff, fs, order=5):\n",
        "    b, a = butter_lowpass(cutoff, fs, order=order)\n",
        "    y = lfilter(b, a, data)\n",
        "    return y\n",
        "\n",
        "\n",
        "# Parameters for the Butterworth filter\n",
        "cutoff_frequency = 0.1  # Define an appropriate cutoff frequency\n",
        "sampling_rate = 360  # Define the sampling rate of the ECG data\n",
        "filter_order = 5  # Define the order of the filter\n",
        "\n",
        "# AAMI heartbeat classification mapping based on MIT-BIH annotations\n",
        "aami_mapping = {\n",
        "    'N': 'N', 'L': 'N', 'R': 'N', 'e': 'N', 'j': 'N',  # Non-ectopic beats\n",
        "    'A': 'S', 'a': 'S', 'J': 'S', 'S': 'S',             # Supraventricular ectopic beats\n",
        "    'V': 'V', 'E': 'V', '!': 'V', '[': 'V', ']': 'V',   # Ventricular ectopic beats\n",
        "    'F': 'F',                                           # Fusion beats\n",
        "    '/': 'Q', 'f': 'Q', 'Q': 'Q'                        # Unknown beats\n",
        "}\n",
        "\n",
        "# Initialize lists to store processed data\n",
        "all_heartbeats = []\n",
        "all_labels = []\n",
        "\n",
        "# Define the file IDs for records to process\n",
        "file_ids = [str(x) for x in [100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
        "                             111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
        "                             121, 122, 123, 124,\n",
        "                             200, 201, 202, 203, 205, 207, 208, 209, 210,\n",
        "                             212, 213, 214, 215, 217, 219, 220, 221, 222, 223, 228, 230, 231, 232, 233, 234]]\n",
        "\n",
        "# Process each ECG record\n",
        "for file_id in file_ids:\n",
        "    record_name = file_id\n",
        "    try:\n",
        "        # Load ECG data and annotations\n",
        "        signals, fields = wfdb.rdsamp(record_name, sampfrom=0, channels=[0])\n",
        "        annotation = wfdb.rdann(record_name, 'atr')\n",
        "\n",
        "        # Find R-peaks in the ECG signal (assuming normal ECG signal without filtering)\n",
        "        r_peaks, _ = find_peaks(signals[:, 0], distance=fields['fs']*0.6)\n",
        "\n",
        "        # Extract heartbeats based on R-peaks\n",
        "        for r_peak in r_peaks:\n",
        "            start, end = r_peak - 128, r_peak + 108\n",
        "            if start >= 0 and end <= len(signals):\n",
        "                heartbeat = signals[start:end, 0]\n",
        "                all_heartbeats.append(heartbeat)\n",
        "\n",
        "                # Find the closest annotation to the R-peak\n",
        "                annotation_index = np.argmin(np.abs(annotation.sample - r_peak))\n",
        "                symbol = annotation.symbol[annotation_index]\n",
        "                all_labels.append(symbol)\n",
        "\n",
        "    except FileNotFoundError:\n",
        "        print(f\"File '{record_name}' not found. Skipping...\")\n",
        "\n",
        "# Map the annotations to AAMI classes and count them\n",
        "aami_label_counts = Counter()\n",
        "for symbol in all_labels:\n",
        "    if symbol in aami_mapping:\n",
        "        aami_class = aami_mapping[symbol]\n",
        "        aami_label_counts[aami_class] += 1\n",
        "\n",
        "# Print the count for each AAMI class\n",
        "print(\"AAMI Label Counts after categorization:\")\n",
        "for aami_class, count in aami_label_counts.items():\n",
        "    print(f\"{aami_class}: {count}\")\n",
        "\n",
        "# Print the total number of heartbeats categorized\n",
        "print(f\"Total number of heartbeats categorized: {len(all_heartbeats)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-UqB90xuRvK",
        "outputId": "81f60019-a48d-4b1d-a9e3-53f985c79d6d"
      },
      "id": "y-UqB90xuRvK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AAMI Label Counts after categorization:\n",
            "N: 87326\n",
            "S: 2651\n",
            "V: 6103\n",
            "Q: 8042\n",
            "F: 782\n",
            "Total number of heartbeats categorized: 105750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Collection**"
      ],
      "metadata": {
        "id": "FOHb2xwZ1k7g"
      },
      "id": "FOHb2xwZ1k7g"
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/MyDrive/ECG"
      ],
      "metadata": {
        "id": "tlvEvtl_0taT"
      },
      "id": "tlvEvtl_0taT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Segmentation**"
      ],
      "metadata": {
        "id": "0CImX4444Pmd"
      },
      "id": "0CImX4444Pmd"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "ebf78fcc"
      },
      "outputs": [],
      "source": [
        "\n",
        "for record_number in [100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
        "                             111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
        "                             121, 122, 123, 124,\n",
        "                             200, 201, 202, 203, 205, 207, 208, 209, 210,\n",
        "                             212, 213, 214, 215, 217, 219, 220, 221, 222, 223, 228, 230, 231, 232, 233, 234]:\n",
        "\n",
        "    record_name = str(record_number)\n",
        "    try:\n",
        "        # Load ECG data from the .dat file\n",
        "        record = wfdb.rdrecord(record_name)\n",
        "        ecg_data = record.p_signal[:, 0]\n",
        "\n",
        "\n",
        "        # Load annotations from the .atr file\n",
        "        annotation = wfdb.rdann(record_name, atr_file_extension)\n",
        "\n",
        "\n",
        "        # Normalize the ECG signal to the range [0, 1]\n",
        "        ecg_data = normalize_data(ecg_data)\n",
        "\n",
        "        # Find R-peaks in the ECG signal\n",
        "        r_peaks, _ = find_peaks(ecg_data, height=0.2, distance=200)\n",
        "\n",
        "        for r_peak in r_peaks:\n",
        "            total_rpeaks = total_rpeaks + 1\n",
        "            # Determine the annotation label for the current R-peak\n",
        "            annotation_indices = np.where(annotation.sample == r_peak)[0]\n",
        "\n",
        "            if annotation_indices.size > 0:\n",
        "                # Use the first matching annotation index\n",
        "                annotation_index = annotation_indices[0]\n",
        "                annotation_label = annotation.symbol[annotation_index]\n",
        "\n",
        "\n",
        "            start = r_peak - 200\n",
        "            end = r_peak + 200\n",
        "            if start < 0 or end >= len(ecg_data):\n",
        "                continue\n",
        "\n",
        "            heartbeat_segment = ecg_data[start:end]\n",
        "            heartbeats.append(heartbeat_segment)\n",
        "            labels.append(annotation_label)\n",
        "\n",
        "        aami_categories = {'N': [], 'S': [], 'V': [], 'F': [], 'Q': []}\n",
        "\n",
        "        for i in range(len(heartbeats)):\n",
        "            annotation_l = labels[i]\n",
        "            heartbeat = heartbeats[i]\n",
        "\n",
        "            if annotation_l in ['N', 'L', 'R', 'e', 'j']:\n",
        "                aami_categories['N'].append(heartbeat)\n",
        "\n",
        "            elif annotation_l in ['V', 'E']:\n",
        "                aami_categories['V'].append(heartbeat)\n",
        "\n",
        "            elif annotation_l in ['A', 'a', 'J', 'S']:\n",
        "                aami_categories['S'].append(heartbeat)\n",
        "\n",
        "            elif annotation_l == 'F':\n",
        "                aami_categories['F'].append(heartbeat)\n",
        "\n",
        "            elif annotation_l in ['P', '/', 'f', 'Q', 'U']:\n",
        "                aami_categories['Q'].append(heartbeat)\n",
        "\n",
        "            else:\n",
        "                continue\n",
        "\n",
        "    except FileNotFoundError:\n",
        "        # Handle the case where the file does not exist\n",
        "        print(f\"File '{record_name}' not found. Skipping...\")\n"
      ],
      "id": "ebf78fcc"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15c2a059",
        "outputId": "8b29d1bf-d869-4694-f477-900c65a97c27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Category N: 95666 heartbeats\n",
            "Category S: 2485 heartbeats\n",
            "Category V: 8344 heartbeats\n",
            "Category F: 512 heartbeats\n",
            "Category Q: 4292 heartbeats\n",
            "Total segments from all Category 111299\n"
          ]
        }
      ],
      "source": [
        "# Loop through the AAMI categories and print the counts\n",
        "total = 0\n",
        "for category, heartbeats in aami_categories.items():\n",
        "    count = len(heartbeats)\n",
        "    print(f'Category {category}: {count} heartbeats')\n",
        "    total = total + count\n",
        "\n",
        "print(f'Total segments from all Category {total}')\n"
      ],
      "id": "15c2a059"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "ae871432",
        "outputId": "37721a25-348a-4b80-8bfa-8b71a7f3dddc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1500x800 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AAMI Class Type: N, Number of Segments: 95696\n",
            "AAMI Class Type: S, Number of Segments: 2486\n",
            "AAMI Class Type: V, Number of Segments: 8347\n",
            "AAMI Class Type: F, Number of Segments: 512\n",
            "AAMI Class Type: Q, Number of Segments: 4294\n"
          ]
        }
      ],
      "source": [
        "# Define a dictionary to count the number of segments for each annotation type\n",
        "num_segments_per_category = {}\n",
        "\n",
        "# Create a single figure for all annotation types\n",
        "plt.figure(figsize=(15, 8))\n",
        "\n",
        "# Visualize each type of annotation\n",
        "for i, (aami_label, segments) in enumerate(aami_categories.items()):\n",
        "    num_segments = len(segments)\n",
        "    num_segments_per_category[aami_label] = num_segments\n",
        "\n",
        "    for j, segment in enumerate(segments[:1]):\n",
        "        plt.subplot(5, len(aami_categories), i * 5 + j + 1)\n",
        "        plt.plot(segment)\n",
        "        plt.title(f'{aami_label} - Segment {j + 1}')\n",
        "        plt.xlabel('Sample')\n",
        "        plt.ylabel('Amplitude')\n",
        "\n",
        "# Adjust layout and show the figure\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Print the number of segments for each annotation type\n",
        "for aami_label, num_segments in num_segments_per_category.items():\n",
        "    if(aami_label == 'S'):\n",
        "        num_seg_s = num_segments\n",
        "    elif(aami_label == 'F'):\n",
        "        num_seg_f = num_segments\n",
        "    elif(aami_label == 'N'):\n",
        "        num_seg_n = num_segments\n",
        "    elif(aami_label == 'V'):\n",
        "        num_seg_v = num_segments\n",
        "    elif(aami_label == 'Q'):\n",
        "        num_seg_q = num_segments\n",
        "\n",
        "    print(f'AAMI Class Type: {aami_label}, Number of Segments: {num_segments}')"
      ],
      "id": "ae871432"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ed10a8e5"
      },
      "source": [
        "# Data sampling to balance data using oversampling"
      ],
      "id": "ed10a8e5"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "c6a4506c"
      },
      "outputs": [],
      "source": [
        "# Split the data into training (70%), validation (15%), and test (15%) sets\n",
        "X = []  # Features\n",
        "y = []  # Target variable\n",
        "\n",
        "for category, heartbeats in aami_categories.items():\n",
        "    if heartbeats:\n",
        "        # Append the features and labels\n",
        "        X.extend(heartbeats)\n",
        "        y.extend([category] * len(heartbeats))\n",
        "\n",
        "# Convert to numpy arrays\n",
        "X = np.array(X)\n",
        "y = np.array(y)"
      ],
      "id": "c6a4506c"
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)"
      ],
      "metadata": {
        "id": "vixAylCEnfs0"
      },
      "id": "vixAylCEnfs0",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"X_train_reshaped shape:\", X_train.shape)\n",
        "print(\"y_train_encoded shape:\", y_train.shape)\n",
        "print(\"X_val_reshaped shape:\", X_val.shape)\n",
        "print(\"y_val_encoded shape:\", y_val.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRCEj6F9jA0g",
        "outputId": "85712f12-2f6f-467c-8677-295b3d92ac41"
      },
      "id": "SRCEj6F9jA0g",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_reshaped shape: (77909, 400)\n",
            "y_train_encoded shape: (77909,)\n",
            "X_val_reshaped shape: (16695, 400)\n",
            "y_val_encoded shape: (16695,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **CNN-LSTM-Transformer Model**"
      ],
      "metadata": {
        "id": "TpC5IwHd13Vj"
      },
      "id": "TpC5IwHd13Vj"
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv1D, LSTM, Dense, GlobalAveragePooling1D, Add, Dropout, LayerNormalization\n",
        "import tensorflow as tf\n",
        "\n",
        "class MultiHeadSelfAttention(tf.keras.layers.Layer):\n",
        "    def __init__(self, head_size, num_heads):\n",
        "        super(MultiHeadSelfAttention, self).__init__()\n",
        "        self.head_size = head_size\n",
        "        self.num_heads = num_heads\n",
        "        self.attention = tf.keras.layers.MultiHeadAttention(\n",
        "            key_dim=head_size, num_heads=num_heads, dropout=0.1\n",
        "        )\n",
        "        self.layer_norm = LayerNormalization(epsilon=1e-6)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        attention_output = self.attention(inputs, inputs)\n",
        "        x = self.layer_norm(inputs + attention_output)\n",
        "        return x\n",
        "\n",
        "# Define the input layer\n",
        "input_shape = (200, 1)\n",
        "input_layer = Input(shape=input_shape)\n",
        "\n",
        "# 1D CNN layer\n",
        "cnn_output = Conv1D(filters=64, kernel_size=5, activation='relu')(input_layer)\n",
        "\n",
        "# LSTM layer\n",
        "lstm_output = LSTM(64, return_sequences=True)(cnn_output)\n",
        "\n",
        "# Transformer Encoder\n",
        "def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):\n",
        "    # Normalization and Attention\n",
        "    x = LayerNormalization(epsilon=1e-6)(inputs)\n",
        "    x = MultiHeadSelfAttention(head_size=head_size, num_heads=num_heads)(x)\n",
        "    x = Dropout(dropout)(x)\n",
        "    res = Add()([inputs, x])\n",
        "\n",
        "    # Feed Forward Part\n",
        "    x = LayerNormalization(epsilon=1e-6)(res)\n",
        "    x = Conv1D(filters=ff_dim, kernel_size=1, activation=\"relu\")(x)\n",
        "    x = Dropout(dropout)(x)\n",
        "    x = Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)\n",
        "    return Add()([res, x])\n",
        "transformer_output = transformer_encoder(lstm_output, 64, 2, 64, 0.1)\n",
        "\n",
        "# Global Average Pooling\n",
        "global_avg_pooling = GlobalAveragePooling1D()(transformer_output)\n",
        "\n",
        "# Dense layer for classification\n",
        "output_layer = Dense(5, activation='softmax')(global_avg_pooling)\n",
        "\n",
        "# Build the model\n",
        "model = Model(inputs=input_layer, outputs=output_layer)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'], run_eagerly=True)\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFk1YIoIgRBd",
        "outputId": "b89bc199-b0d1-415d-e57b-f744262b6dd3"
      },
      "id": "jFk1YIoIgRBd",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 200, 1)]             0         []                            \n",
            "                                                                                                  \n",
            " conv1d (Conv1D)             (None, 196, 64)              384       ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " lstm (LSTM)                 (None, 196, 64)              33024     ['conv1d[0][0]']              \n",
            "                                                                                                  \n",
            " layer_normalization (Layer  (None, 196, 64)              128       ['lstm[0][0]']                \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " multi_head_self_attention   (None, 196, 64)              33344     ['layer_normalization[0][0]'] \n",
            " (MultiHeadSelfAttention)                                                                         \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (None, 196, 64)              0         ['multi_head_self_attention[0]\n",
            "                                                                    [0]']                         \n",
            "                                                                                                  \n",
            " add (Add)                   (None, 196, 64)              0         ['lstm[0][0]',                \n",
            "                                                                     'dropout[0][0]']             \n",
            "                                                                                                  \n",
            " layer_normalization_2 (Lay  (None, 196, 64)              128       ['add[0][0]']                 \n",
            " erNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " conv1d_1 (Conv1D)           (None, 196, 64)              4160      ['layer_normalization_2[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)         (None, 196, 64)              0         ['conv1d_1[0][0]']            \n",
            "                                                                                                  \n",
            " conv1d_2 (Conv1D)           (None, 196, 64)              4160      ['dropout_1[0][0]']           \n",
            "                                                                                                  \n",
            " add_1 (Add)                 (None, 196, 64)              0         ['add[0][0]',                 \n",
            "                                                                     'conv1d_2[0][0]']            \n",
            "                                                                                                  \n",
            " global_average_pooling1d (  (None, 64)                   0         ['add_1[0][0]']               \n",
            " GlobalAveragePooling1D)                                                                          \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 5)                    325       ['global_average_pooling1d[0][\n",
            "                                                                    0]']                          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 75653 (295.52 KB)\n",
            "Trainable params: 75653 (295.52 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Evaluation**"
      ],
      "metadata": {
        "id": "hcjG3wsC2FMh"
      },
      "id": "hcjG3wsC2FMh"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv1D, LSTM, Dense, GlobalAveragePooling1D, Add, Dropout, LayerNormalization\n",
        "import tensorflow as tf\n",
        "import tensorflow as tf\n",
        "\n",
        "# Clear TensorFlow session\n",
        "tf.keras.backend.clear_session()\n",
        "\n",
        "# Assuming you have your data split as follows:\n",
        "# X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "# X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
        "\n",
        "# Convert labels to one-hot encoding\n",
        "label_binarizer = LabelBinarizer()\n",
        "y_train_encoded = label_binarizer.fit_transform(y_train)\n",
        "y_val_encoded = label_binarizer.transform(y_val)\n",
        "# Assuming you're downsampling by 2\n",
        "# Assuming your original data has shape (samples, features) and you want a window size of 200\n",
        "window_size = 200\n",
        "\n",
        "# Reshape the data to have a window size of 200\n",
        "X_train_reshaped = X_train[:, :window_size].reshape(-1, window_size, 1)\n",
        "X_val_reshaped = X_val[:, :window_size].reshape(-1, window_size, 1)\n",
        "X_test_reshaped = X_test[:, :window_size].reshape(-1, window_size, 1)\n",
        "\n",
        "# Now, you can use X_train_reshaped, X_val_reshaped, and X_test_reshaped in the fit method\n",
        "history = model.fit(\n",
        "    X_train_reshaped, y_train_encoded,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val_reshaped, y_val_encoded)\n",
        ")\n",
        "\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy vs Epoch')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot loss vs epoch for both training and validation sets\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Loss vs Epoch')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Evaluate the model on the reshaped test set\n",
        "test_loss, test_accuracy = model.evaluate(X_test_reshaped, label_binarizer.transform(y_test))\n",
        "print(f'Test Loss: {test_loss}, Test Accuracy: {test_accuracy}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "t5BiwZndhGRz",
        "outputId": "0e74f537-e50a-4d66-b32a-a3a14044b3c4"
      },
      "id": "t5BiwZndhGRz",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2435/2435 [==============================] - 280s 113ms/step - loss: 0.5218 - accuracy: 0.8597 - val_loss: 0.4133 - val_accuracy: 0.8766\n",
            "Epoch 2/10\n",
            "2435/2435 [==============================] - 264s 109ms/step - loss: 0.3779 - accuracy: 0.8864 - val_loss: 0.3476 - val_accuracy: 0.8951\n",
            "Epoch 3/10\n",
            "2435/2435 [==============================] - 261s 107ms/step - loss: 0.3455 - accuracy: 0.8941 - val_loss: 0.3576 - val_accuracy: 0.8897\n",
            "Epoch 4/10\n",
            "2435/2435 [==============================] - 259s 106ms/step - loss: 0.3248 - accuracy: 0.8979 - val_loss: 0.3141 - val_accuracy: 0.9016\n",
            "Epoch 5/10\n",
            "2435/2435 [==============================] - 262s 107ms/step - loss: 0.3048 - accuracy: 0.9036 - val_loss: 0.2956 - val_accuracy: 0.9078\n",
            "Epoch 6/10\n",
            "2435/2435 [==============================] - 259s 106ms/step - loss: 0.2898 - accuracy: 0.9066 - val_loss: 0.2967 - val_accuracy: 0.9039\n",
            "Epoch 7/10\n",
            "2435/2435 [==============================] - 258s 106ms/step - loss: 0.2782 - accuracy: 0.9095 - val_loss: 0.2899 - val_accuracy: 0.9045\n",
            "Epoch 8/10\n",
            "2435/2435 [==============================] - 261s 107ms/step - loss: 0.2688 - accuracy: 0.9115 - val_loss: 0.2570 - val_accuracy: 0.9158\n",
            "Epoch 9/10\n",
            "2435/2435 [==============================] - 263s 108ms/step - loss: 0.2585 - accuracy: 0.9146 - val_loss: 0.2613 - val_accuracy: 0.9161\n",
            "Epoch 10/10\n",
            "2435/2435 [==============================] - 264s 108ms/step - loss: 0.2513 - accuracy: 0.9149 - val_loss: 0.2427 - val_accuracy: 0.9169\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "522/522 [==============================] - 18s 34ms/step - loss: 0.2454 - accuracy: 0.9156\n",
            "Test Loss: 0.24537886679172516, Test Accuracy: 0.9156034588813782\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_metric(history, metric, label, ylabel, title):\n",
        "    plt.plot(history.history[metric], label=f'Training {label}')\n",
        "    plt.plot(history.history[f'val_{metric}'], label=f'Validation {label}')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel(ylabel)\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "# Plot accuracy vs epoch for both training and validation sets\n",
        "plot_metric(history, 'accuracy', 'Accuracy', 'Accuracy', 'Model Accuracy of CNN-LSTM-Transformer Encoder')\n",
        "\n",
        "# Plot loss vs epoch for both training and validation sets\n",
        "plot_metric(history, 'loss', 'Loss', 'Loss', 'Model Loss of CNN-LSTM-Transformer Encoder')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "b0aV21eg7nOv",
        "outputId": "e81cf5ba-1d5a-47e9-b2db-9b71a6e8c1c4"
      },
      "id": "b0aV21eg7nOv",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], color='blue', label='Train')\n",
        "plt.plot(history.history['val_accuracy'], color='red', label='Validation')\n",
        "plt.title('Model Accuracy of CNN-LSTM-Transformer Encoder')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], color='green', label='Train')\n",
        "plt.plot(history.history['val_loss'], color='orange', label='Validation')\n",
        "plt.title('Model Loss of CNN-LSTM-Transformer Encoder')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "zYqbFT2O8erd",
        "outputId": "7defb1e7-cd98-4422-a687-44323c722b24"
      },
      "id": "zYqbFT2O8erd",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming 'history' is the object returned by model.fit\n",
        "# Plot training & validation loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "MZKpkNwevw9L",
        "outputId": "58d18274-29e6-4a9a-a42d-273a35ef0df2"
      },
      "id": "MZKpkNwevw9L",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-78cb24b286dc>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Assuming 'history' is the object returned by model.fit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Plot training & validation loss values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model Loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'history' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv1D, LSTM, Dense, GlobalAveragePooling1D, Add, Dropout, LayerNormalization\n",
        "import tensorflow as tf  # Add this import for MultiHeadAttention\n",
        "from tensorflow.keras.layers import Bidirectional\n",
        "\n",
        "\n",
        "class MultiHeadSelfAttention(tf.keras.layers.Layer):\n",
        "    def __init__(self, head_size, num_heads):\n",
        "        super(MultiHeadSelfAttention, self).__init__()\n",
        "        self.head_size = head_size\n",
        "        self.num_heads = num_heads\n",
        "        self.attention = tf.keras.layers.MultiHeadAttention(\n",
        "            key_dim=head_size, num_heads=num_heads, dropout=0.1\n",
        "        )\n",
        "        self.layer_norm = LayerNormalization(epsilon=1e-6)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        attention_output = self.attention(inputs, inputs)\n",
        "        x = self.layer_norm(inputs + attention_output)\n",
        "        return x\n",
        "\n",
        "# Define the input layer\n",
        "input_shape = (200, 1)\n",
        "input_layer = Input(shape=input_shape)\n",
        "\n",
        "# 1D CNN layer\n",
        "cnn_output = Conv1D(filters=64, kernel_size=5, activation='relu')(input_layer)\n",
        "cnn_output_2 = Conv1D(filters=32, kernel_size=5, activation='relu')(cnn_output)\n",
        "\n",
        "\n",
        "# LSTM layer\n",
        "lstm_output = Bidirectional(LSTM(64, return_sequences=True))(cnn_output_2)\n",
        "\n",
        "\n",
        "# Transformer Encoder\n",
        "def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):\n",
        "    # Normalization and Attention\n",
        "    x = LayerNormalization(epsilon=1e-6)(inputs)\n",
        "    x = MultiHeadSelfAttention(head_size=head_size, num_heads=num_heads)(x)  # Corrected this line\n",
        "    x = Dropout(dropout)(x)\n",
        "    res = Add()([inputs, x])\n",
        "\n",
        "    # Feed Forward Part\n",
        "    x = LayerNormalization(epsilon=1e-6)(res)\n",
        "    x = Conv1D(filters=ff_dim, kernel_size=1, activation=\"relu\")(x)\n",
        "    x = Dropout(dropout)(x)\n",
        "    x = Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)\n",
        "    return Add()([res, x])\n",
        "\n",
        "transformer_output = transformer_encoder(lstm_output, 64, 2, 64, 0.1)\n",
        "\n",
        "# Global Average Pooling\n",
        "global_avg_pooling = GlobalAveragePooling1D()(transformer_output)\n",
        "\n",
        "# Dense layer for classification\n",
        "output_layer = Dense(5, activation='softmax')(global_avg_pooling)  # Assuming 5 classes (N, S, V, F, Q)\n",
        "\n",
        "# Build the model\n",
        "model = Model(inputs=input_layer, outputs=output_layer)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'], run_eagerly=True)\n",
        "\n",
        "# Print the model summary\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VKFIbLhQ2tBR",
        "outputId": "ab9071c5-b8ff-437b-b540-8d3f0e24b499"
      },
      "id": "VKFIbLhQ2tBR",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)        [(None, 200, 1)]             0         []                            \n",
            "                                                                                                  \n",
            " conv1d_4 (Conv1D)           (None, 196, 64)              384       ['input_2[0][0]']             \n",
            "                                                                                                  \n",
            " conv1d_5 (Conv1D)           (None, 192, 32)              10272     ['conv1d_4[0][0]']            \n",
            "                                                                                                  \n",
            " bidirectional_1 (Bidirecti  (None, 192, 128)             49664     ['conv1d_5[0][0]']            \n",
            " onal)                                                                                            \n",
            "                                                                                                  \n",
            " layer_normalization_3 (Lay  (None, 192, 128)             256       ['bidirectional_1[0][0]']     \n",
            " erNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " multi_head_self_attention_  (None, 192, 128)             66304     ['layer_normalization_3[0][0]'\n",
            " 1 (MultiHeadSelfAttention)                                         ]                             \n",
            "                                                                                                  \n",
            " dropout_2 (Dropout)         (None, 192, 128)             0         ['multi_head_self_attention_1[\n",
            "                                                                    0][0]']                       \n",
            "                                                                                                  \n",
            " add_2 (Add)                 (None, 192, 128)             0         ['bidirectional_1[0][0]',     \n",
            "                                                                     'dropout_2[0][0]']           \n",
            "                                                                                                  \n",
            " layer_normalization_5 (Lay  (None, 192, 128)             256       ['add_2[0][0]']               \n",
            " erNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " conv1d_6 (Conv1D)           (None, 192, 64)              8256      ['layer_normalization_5[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " dropout_3 (Dropout)         (None, 192, 64)              0         ['conv1d_6[0][0]']            \n",
            "                                                                                                  \n",
            " conv1d_7 (Conv1D)           (None, 192, 128)             8320      ['dropout_3[0][0]']           \n",
            "                                                                                                  \n",
            " add_3 (Add)                 (None, 192, 128)             0         ['add_2[0][0]',               \n",
            "                                                                     'conv1d_7[0][0]']            \n",
            "                                                                                                  \n",
            " global_average_pooling1d (  (None, 128)                  0         ['add_3[0][0]']               \n",
            " GlobalAveragePooling1D)                                                                          \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 5)                    645       ['global_average_pooling1d[0][\n",
            "                                                                    0]']                          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 144357 (563.89 KB)\n",
            "Trainable params: 144357 (563.89 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv1D, LSTM, Dense, GlobalAveragePooling1D, Add, Dropout, LayerNormalization\n",
        "import tensorflow as tf\n",
        "import tensorflow as tf\n",
        "\n",
        "# Clear TensorFlow session\n",
        "tf.keras.backend.clear_session()\n",
        "\n",
        "# Assuming you have your data split as follows:\n",
        "# X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "# X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
        "\n",
        "# Convert labels to one-hot encoding\n",
        "label_binarizer = LabelBinarizer()\n",
        "y_train_encoded = label_binarizer.fit_transform(y_train)\n",
        "y_val_encoded = label_binarizer.transform(y_val)\n",
        "# Assuming you're downsampling by 2\n",
        "# Assuming your original data has shape (samples, features) and you want a window size of 200\n",
        "window_size = 200\n",
        "\n",
        "# Reshape the data to have a window size of 200\n",
        "X_train_reshaped = X_train[:, :window_size].reshape(-1, window_size, 1)\n",
        "X_val_reshaped = X_val[:, :window_size].reshape(-1, window_size, 1)\n",
        "X_test_reshaped = X_test[:, :window_size].reshape(-1, window_size, 1)\n",
        "\n",
        "# Now, you can use X_train_reshaped, X_val_reshaped, and X_test_reshaped in the fit method\n",
        "history = model.fit(\n",
        "    X_train_reshaped, y_train_encoded,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_val_reshaped, y_val_encoded)\n",
        ")\n",
        "\n",
        "# Evaluate the model on the reshaped test set\n",
        "test_loss, test_accuracy = model.evaluate(X_test_reshaped, label_binarizer.transform(y_test))\n",
        "print(f'Test Loss: {test_loss}, Test Accuracy: {test_accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "soaYh1pZ4Pn9",
        "outputId": "348553ab-b902-4a3e-c774-8ceecffa9980"
      },
      "id": "soaYh1pZ4Pn9",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2435/2435 [==============================] - 330s 134ms/step - loss: 0.4830 - accuracy: 0.8718 - val_loss: 0.4207 - val_accuracy: 0.8820\n",
            "Epoch 2/10\n",
            "2435/2435 [==============================] - 320s 131ms/step - loss: 0.3795 - accuracy: 0.8910 - val_loss: 0.3692 - val_accuracy: 0.8924\n",
            "Epoch 3/10\n",
            "2435/2435 [==============================] - 319s 131ms/step - loss: 0.3479 - accuracy: 0.8976 - val_loss: 0.3577 - val_accuracy: 0.8966\n",
            "Epoch 4/10\n",
            "2435/2435 [==============================] - 318s 131ms/step - loss: 0.4324 - accuracy: 0.8789 - val_loss: 0.3511 - val_accuracy: 0.8982\n",
            "Epoch 5/10\n",
            "2435/2435 [==============================] - 339s 139ms/step - loss: 0.3435 - accuracy: 0.8957 - val_loss: 0.3340 - val_accuracy: 0.9042\n",
            "Epoch 6/10\n",
            "2435/2435 [==============================] - 316s 130ms/step - loss: 0.3159 - accuracy: 0.9031 - val_loss: 0.3114 - val_accuracy: 0.9012\n",
            "Epoch 7/10\n",
            "2435/2435 [==============================] - 318s 130ms/step - loss: 0.2985 - accuracy: 0.9083 - val_loss: 0.3290 - val_accuracy: 0.9012\n",
            "Epoch 8/10\n",
            "2435/2435 [==============================] - 317s 130ms/step - loss: 0.3138 - accuracy: 0.9045 - val_loss: 0.3282 - val_accuracy: 0.9030\n",
            "Epoch 9/10\n",
            "2435/2435 [==============================] - 324s 133ms/step - loss: 0.3518 - accuracy: 0.8980 - val_loss: 0.3674 - val_accuracy: 0.8901\n",
            "Epoch 10/10\n",
            "2435/2435 [==============================] - 325s 134ms/step - loss: 0.3314 - accuracy: 0.9009 - val_loss: 0.3208 - val_accuracy: 0.9048\n",
            "522/522 [==============================] - 24s 47ms/step - loss: 0.3231 - accuracy: 0.9043\n",
            "Test Loss: 0.32313936948776245, Test Accuracy: 0.9043425917625427\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OQuvtEbo2tIn"
      },
      "id": "OQuvtEbo2tIn",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"X_train_reshaped shape:\", X_train.shape)\n",
        "print(\"y_train_encoded shape:\", y_train_encoded.shape)\n",
        "print(\"X_val_reshaped shape:\", X_val.shape)\n",
        "print(\"y_val_encoded shape:\", y_val_encoded.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZF6EDfZi70I",
        "outputId": "d54fd853-5cec-47d7-a3bf-1e728d165a5c"
      },
      "id": "PZF6EDfZi70I",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_reshaped shape: (77909, 400)\n",
            "y_train_encoded shape: (77909, 5)\n",
            "X_val_reshaped shape: (16695, 400)\n",
            "y_val_encoded shape: (16695, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VWlPJJ6FXg6h"
      },
      "outputs": [],
      "source": [
        "X_samples = []  # Features\n",
        "y_samples = []  # Target variable\n",
        "categories_extracted = set()  # Keep track of categories for which samples have been extracted\n",
        "\n",
        "for category, heartbeats in aami_categories.items():\n",
        "    if heartbeats and category not in categories_extracted:\n",
        "        # Append the features and labels\n",
        "        X_samples.append(heartbeats[0])  # Extract the first sample for each category\n",
        "        y_samples.append(category)\n",
        "        categories_extracted.add(category)\n",
        "\n",
        "# Convert to numpy arrays\n",
        "X_samples = np.array(X_samples)\n",
        "y_samples = np.array(y_samples)\n",
        "\n",
        "df = pd.DataFrame(data=X_samples, columns=[f'value{i}' for i in range(1, X_samples.shape[1] + 1)])\n",
        "df['label'] = y_samples\n",
        "\n",
        "# Save the entire dataset to a CSV file\n",
        "df.to_csv('sample.csv', index=False)"
      ],
      "id": "VWlPJJ6FXg6h"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "# Assuming you have X and y as your input features and target variables\n",
        "\n",
        "# Split the data into training (70%), validation (15%), and test (15%) sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
        "\n",
        "# Reshape your input data to (batch_size, sequence_length, input_dim)\n",
        "X_train = np.expand_dims(X_train, axis=-1)\n",
        "X_val = np.expand_dims(X_val, axis=-1)\n",
        "X_test = np.expand_dims(X_test, axis=-1)\n",
        "\n",
        "# Define the CNN part of the model\n",
        "cnn_input = layers.Input(shape=(X_train.shape[1], 1))\n",
        "cnn_output = layers.Conv1D(filters=64, kernel_size=3, activation='relu')(cnn_input)\n",
        "cnn_output = layers.MaxPooling1D(pool_size=2)(cnn_output)\n",
        "cnn_output = layers.Flatten()(cnn_output)\n",
        "\n",
        "# Define the Transformer part of the model\n",
        "transformer_input = layers.Input(shape=(X_train.shape[1], 1))\n",
        "transformer_output = layers.MultiHeadAttention(num_heads=4, key_dim=4)(transformer_input, transformer_input)\n",
        "transformer_output = layers.GlobalAveragePooling1D()(transformer_output)\n",
        "\n",
        "# Concatenate the outputs of CNN and Transformer\n",
        "combined_output = layers.Concatenate()([cnn_output, transformer_output])\n",
        "\n",
        "# Add a Dense layer for classification\n",
        "dense_output = layers.Dense(128, activation='relu')(combined_output)\n",
        "output = layers.Dense(5, activation='softmax')(dense_output)\n",
        "\n",
        "# Create the model\n",
        "model = keras.Model(inputs=[cnn_input, transformer_input], outputs=output)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit([X_train, X_train], y_train, epochs=10, validation_data=([X_val, X_val], y_val))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ak9IlTv8pvb4",
        "outputId": "02fb8456-76d8-4ee7-bf3a-63b6ef7626b7"
      },
      "id": "Ak9IlTv8pvb4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "UnimplementedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnimplementedError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-a5c89863a59e>\u001b[0m in \u001b[0;36m<cell line: 42>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m     \u001b[0mkeras_symbolic_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_keras_symbolic_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkeras_symbolic_tensors\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m       raise core._SymbolicException(\n",
            "\u001b[0;31mUnimplementedError\u001b[0m: Graph execution error:\n\nDetected at node Cast_1 defined at (most recent call last):\n  File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n\n  File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n\n  File \"/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py\", line 37, in <module>\n\n  File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n\n  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n\n  File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\n\n  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n\n  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n\n  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n\n  File \"<ipython-input-44-a5c89863a59e>\", line 42, in <cell line: 42>\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1783, in fit\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1377, in train_function\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1360, in step_function\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1349, in run_step\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1131, in train_step\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1225, in compute_metrics\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/compile_utils.py\", line 620, in update_state\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/metrics_utils.py\", line 77, in decorated\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/metrics/base_metric.py\", line 140, in update_state_fn\n\n  File \"/usr/local/lib/python3.10/dist-packages/keras/src/metrics/base_metric.py\", line 708, in update_state\n\n2 root error(s) found.\n  (0) UNIMPLEMENTED:  Cast string to float is not supported\n\t [[{{node Cast_1}}]]\n  (1) CANCELLED:  Function was cancelled before it was started\n0 successful operations.\n0 derived errors ignored. [Op:__inference_train_function_5665]"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}